Automatic shape modeling from point clouds has been explored extensively for various applications [1]}. PolyFit was proposed for reconstructing lightweight polygonal surfaces from point clouds [2]}. Topological priors were recently incorporated into point scans for reconstructing point cloud surfaces [3]}.
The automatic modeling approaches show robust and satisfying reconstruction results for dense point clouds. However, these approaches may fail to provide a feasible solution for sparse point clouds, and we found that the human factor can greatly improve modeling results due to users' spatial cognitive abilities.
